Manuel Gil

The paper evaluates ARPIP's ability to reconstruct ancestral sequences with long indels, showing it preserves gap length distributions and captures deletion bias in mammalian proteins.

This paper introduces indelMaP, a method that improves ancestral sequence reconstruction and multiple sequence alignment by treating insertions and deletions as distinct evolutionary events, achieving high accuracy and speed for densely sampled datasets.

The paper introduces a multi-channel deep neural network for biomedical relation extraction that outperforms existing methods by leveraging textual and graph features.

This paper introduces a combinatorial optimization method to jointly infer multiple sequence alignments and phylogenetic trees under maximum likelihood, addressing computational limitations of existing approaches.

The paper introduces a semantic model and federated approach to integrate heterogeneous biological databases, enabling joint queries across gene expression, orthology, and protein data sources.